Automatic detection of parsimony for heteroskedastic time series processes |
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Authors: | R Östermark |
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Affiliation: | ?bo Akademi University, Department of Business Administration, Henriksgatan 7, FIN-20500 ?BO, Finland E-mail: ralf.ostermark@abo.fi, FI
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Abstract: | The paper proposes a new multiple-representation geno-mathematical algorithm for coping with ill-conditioned time series
processes through competing nonlinear model formulations. Extensive testing and comparisons to a rigorous statistical time
series package indicate that the geno-mathematical search-machine is effective and robust for modelling complicated time series.
The new algorithm is used to model a representative set of global asset returns. The diagnostic tests prove that the ARCH-effects
of the difficult nonlinear processes are annihilated completely in both full and reduced model variants. |
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Keywords: | Automatic model detection Genetic computation Heteroskedastic time series Multiple competing representations |
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